Analyzing Defects of Solar Panels under Natural Atmospheric Conditions with Thermal Image Processing

Abstract

Abstract-Sun is an ultimate source of energy, which is clean, inexhaustible and safe for environment. The energy obtained from sun is known as solar energy. When solar radiations fall on earth surface solar cells convert these solar radiations into electrical energy. Solar cells are one of the important components of solar panels. Many solar cells combine in series or in parallel to form solar module and solar panels. A solar photovoltaic array is a combination of solar panels and is installed in open atmospheric condition. The natural conditions such as dust and dirt, shade of tree affect operation of solar panels. These natural conditions cannot be avoided but can be analyzed using visual inspection and thermal camera. The visual inspection approach is useful only when the defects are visible by naked human eye but when defects cannot be visualized by naked human eye thermo-graphical approach is used. This paper discusses the identification of various defects in solar panels by applying image processing technique applied for thermal images under natural atmospheric conditions for visual inspection, shading effect of tree, dust and dirt deposition effects on solar panels using thermal imaging camera.

Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions

“…Considering the fact that creating quality datasets for urban scene understanding is a labor-intensive task, especially, for datasets designed for autonomous driving applications and model fine tuning before their practical applications, we proposed to limit object classes to only necessary ones. The further question can be raised about excluding some irrelevant classes (like a sky, a building, a vegetation and sidewalk) in some applications like emergency braking and anti-collision systems [20][21][22] and other applications [23][24][25][26].…”

Defect Detection in Solar Photovoltaic Systems Using Unmanned Aerial Vehicles and Machine Learning

“…Our survey of the related recent efforts suggests that the literature can be broadly categorized into two main clusters, primarily based on the fault detection methods employed to analyze the UAV-collected images. The first cluster relies on segmentation-based approaches using computer vision algorithms, image filtering methods, and statistical analyses of key image features in order to identify and further localize the defect patterns in solar modules (Aghaei et al, 2015, Addabbo et al, 2017, Li et al, 2017, Tsanakas et al, 2017, Liao and Lu, 2021.…”

“…Those two fault modes are prevalent in solar PV systems. In specific, shading is often caused by nearby structures, local clouds, as well as nearby trees and plants, especially when located in agricultural lands in the context of the so-called agrivoltaics (Chaudhary and Chaturvedi, 2018). On the other hand, snow coverage and accumulation on solar modules commonly takes place in PV systems especially when installed in cold climates where the weather conditions allow for frequent snowfall (Øgaard et al, 2021).…”

Solar Panels Crack Detection using Overhead Images

“…Thermal image processing can also be used to analyze the defects of solar panels. The study [4] explains the basic working of solar panels and explains the laws of thermology. They present a detailed report on the identification of defects through visual and infrared thermology.…”